Kombinasi Algoritma Nearest Neighbor Dan Metode Case-Based Reasoning Pada Expert System (Studi Kasus : Asupan Gizi Harian Ibu Hamil)

Muhammad, Farhansyah (2022) Kombinasi Algoritma Nearest Neighbor Dan Metode Case-Based Reasoning Pada Expert System (Studi Kasus : Asupan Gizi Harian Ibu Hamil). Undergraduate Thesis thesis, Institut Teknologi Telkom Purwokerto.

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Abstract

In making a computerized system, especially an expert system, innovations are needed in order to get better results. In this case, we discuss the application of a combination of the nearest neighbor algorithm and the Case-Based Reasoning (CBR) method, where the nearest neighbor algorithm functions to classify problems while CBR functions as a method for solving problems that have been classified with the nearest neighbor algorithm. This method is a method that can draw conclusions based on previous experience. In this case, it discusses health problems, namely the nutritional intake of pregnant women. In Indonesia in 2017, the number of cases of LBW (low birth weight) was 6.2%, where the largest contributor area was the province of Central Sulawesi, which was 8.9%. One of the causes of LBW is pregnant women who are malnourished, a pregnant woman who has low nutritional status has a 4.27 times higher risk of giving birth to LBW babies and the ignorance of the pregnant woman about nutritional intake is one of the causes of this problem. In this study, the expert system will provide information and advice regarding the intake consumed by pregnant women quickly and practically. Obtained an accuracy value of 94%. Using a confusion matrix test scenario with a total of 16 testing data. Keywords: Nutrition, Pregnant women, Expert system, Case-based reasoning, Nearest neighbor.

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